Computer implemented planning systems are widely used for factory, enterprise and supply chain planning functions. In general, the systems model the manufacturing environment and provide plans for producing items to fulfill consumer demand within the constraints of the environment.
A classic artificial intelligence search can include elements such as: an initial state, a goal state, a termination criteria, and a set of moves between states of the search space. For example, in a "blocks world" problem space, an artificial intelligence search engine could have an initial state of a red block and blue block on a table and a yellow block on top of the blue block. A goal state for such an engine could be the goal of building a planned sequence of moves which place the red block on top of the blue block. The termination criteria could be to achieve the goal within 10 minutes or quit. The set of moves could comprise: moving an uncovered block onto the table and moving an uncovered block from the table onto another uncovered block. An artificial intelligence search process, then, could compute a plan for the required moves to achieve the goal. When applied to planning problems, the number of "blocks" greatly increases, and the initial and goal states become more complex.
With more "blocks" and/or more complex initial and goal states, this type of search can be computationally challenging. Further complexity can be added to a system when the types and numbers of moves grow.